When there is a vehicle on a highway in front of a host (i.e. following) vehicle, it is desirable for the driver of the host vehicle to know the intensions of the driver of the in-front (i.e. the target) vehicle as to whether the target vehicle intends to change lanes. If the target vehicle intends to change lanes very soon, then there is no need for the host vehicle to slow down. Unfortunately, the target vehicle may slow down before changing lanes, causing the host vehicle to brake quickly and hard.
Vehicle control and driver awareness systems such as Adaptive Cruise Control (ACC) and Forward Collision Warning (FCW) systems have been developed to aid the host driver to automatically determine the intensions of a target vehicle. Unfortunately, ACC and FCW systems exhibit poor performance when reacting to drivable stationary objects or objects that are leaving the lane of a host vehicle. In the case of ACC, the host vehicle may be forced to break as a result of detecting a stationary object that is not an obstacle. Also, it is undesirable for a host vehicle to execute hard braking when the target vehicle decelerates before leaving the same lane. In an FCW system, false alarms and nuisance alarms are common. An unacceptably high rate of false and nuisance alarms can cause the driver to turn the system off or just ignore the system warning because of lack of trust in system performance. The main source of false alarms is high objects such as overpasses, road signs, traffic lights, and hanging telephone lines. The main source for nuisance alarms is a target vehicle that starts to decelerate without performing a turn.
Under normal (unaided) driving conditions, host vehicle drivers have the ability to recognize that by the time the driver reaches a place to turn, the target vehicle has already moved away from the path of the driver. Further, drivers have the ability to adapt. Therefore, the performance of aided driving systems such as ACC and FCW can be improved when the target of interest (the target vehicle) is validated and characterized by the aid of vision data.
The use of multi-modality sensor data for intelligent vehicle applications is widely known. In A. Broggi and P. Cerri, “A radar driven fusion with vision for vehicle detection,” in PReVENT Fusion e-Journal, 1:17-18, September 2006 (hereinafter “Broggi and Cerri”) and in A. Sole, G. P. Stein, H. Kumon, Y. Tamatsu, and A. Shashua, “Solid or not solid: Vision for Radar Target Validation,” in IEEE Intelligent Vehicles Symposium, Parma, Italy, Jun. 14-17, 2004 (hereinafter “Sole et al.”), radar and vision systems are combined for vehicle detection and validation. However, the system of Broggi and Cerri does not update changes in the geometry of the moving target vehicle, but instead relies solely on pre-calibrated imaging geometry, which leads to inaccuracies, and further, road roughness may cause the failure of target mapping. Accuracy is also diminished when low level image features such as horizontal and vertical lines are employed in Sole et al. or when symmetry and heuristic methods are employed in Broggi and Cerri for vehicle detection. In J. C. McCall, D. Wipf, M. M. Trivedi, and B. Rao: “Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning,” IEEE CVPR Workshop: Machine Vision for Intelligent Vehicles, vol. 3, pp 59-67, 2005 (hereinafter “McCall et al.”) and D. D. Salvucci: “inferring driver intent: A case study in lane-change detection,” Proceedings of the Human Factors Ergonomics Society 48th Annual Meeting, 2004 (hereinafter “Salvucci”), attempts are made to infer the intensions of a driver to make a lane change by using multi-modal data (e.g., road scene, CAN data, eye movement, etc.), which leads to predicting a vehicle's lane changing move ahead of time. However, McCall et al. and Salvucci are applicable only to host vehicles instead of a target vehicle. The driver in a target vehicle cannot be monitored by one or more sensors in the host vehicle in either McCall et al. or Salvucci.
Accordingly, what would be desirable, but has not yet been provided, are an accurate method and resulting system for detecting that a target vehicle is in the lane of and ahead of the host vehicle, and for determining if the target vehicle intends to change lanes.